All articles published by MDPI are made immediately available worldwide under an open access license.Singh, S.Bhardwaj, AnshumanSingh, A.Sam, LydiaShekhar, M.Martín Torres, JavierZorzano, María-PazUnidad de Excelencia Científica María de Maeztu Centro de Astrobiología del Instituto Nacional de Técnica Aeroespacial y CSIC, MDM-2017-07372021-04-132021-04-132019-12-04Remote Sensing 11(24): 2889(2020)https://www.mdpi.com/2072-4292/11/24/2889http://hdl.handle.net/20.500.12666/319The surface and near-surface air temperature observations are primary data for glacio-hydro-climatological studies. The in situ air temperature (T-a) observations require intense logistic and financial investments, making it sparse and fragmented particularly in remote and extreme environments. The temperatures in Himalaya are controlled by a complex system driven by topography, seasons, and cryosphere which further makes it difficult to record or predict its spatial heterogeneity. In this regard, finding a way to fill the observational spatiotemporal gaps in data becomes more crucial. Here, we show the comparison of T-a recorded at 11 high altitude stations in Western Himalaya with their respective land surface temperatures (T-s) recorded by Moderate Resolution Imagining Spectroradiometer (MODIS) Aqua and Terra satellites in cloud-free conditions. We found remarkable seasonal and spatial trends in the T-a vs. T-s relationship: (i) T-s are strongly correlated with T-a (R-2 = 0.77, root mean square difference (RMSD) = 5.9 degrees C, n = 11,101 at daily scale and R-2 = 0.80, RMSD = 5.7 degrees C, n = 3552 at 8-day scale); (ii) in general, the RMSD is lower for the winter months in comparison to summer months for all the stations, (iii) the RMSD is directly proportional to the elevations; (iv) the RMSD is inversely proportional to the annual precipitation. Our results demonstrate the statistically strong and previously unreported T-a vs. T-s relationship and spatial and seasonal variations in its intensity at daily resolution for the Western Himalaya. We anticipate that our results will provide the scientists in Himalaya or similar data-deficient extreme environments with an option to use freely available remotely observed T-s products in their models to fill-up the spatiotemporal data gaps related to in situ monitoring at daily resolution. Substituting T-a by T-s as input in various geophysical models can even improve the model accuracy as using spatially continuous satellite derived T-s in place of discrete in situ T-a extrapolated to different elevations using a constant lapse rate can provide more realistic estimates.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/HimalayaLand surface temperatureAir temperatureTopographyModisinfo:eu-repo/semantics/article10.3390/rs112428892072-4292info:eu-repo/semantics/openAccess